Applications related to tracking, navigation, positioning, etc., of mobile devices are becoming increasingly sophisticated. Global navigation satellite systems (GNSS) such as global positioning systems (GPS) are satellite-based systems used for navigation or position determination of a mobile device, based, for example, on trilateration of wireless signals received from satellites to estimate geographic position and heading. Hybrid positioning is also possible using wireless signals from cellular networks or terrestrial sources, such as wireless wide area networks (WWAN), code division multiple access (CDMA), long term evolution (LTE) networks, etc. However, tracking or positioning of a mobile device using these conventional positioning approaches may not be possible, or accuracy may be significantly degraded in locations where such wireless signals are weak or non-existent.
For example, conventional approaches may fail in cases of pedestrian navigation which relies only on external assistance received through wireless signals. In the case of a pedestrian user carrying a mobile device, navigation may be performed with acceptable accuracy and quality when the user is in an environment with strong wireless signals being present. In other words, uncertainty in the user's position is low. However, there are many situations where the signal strength may fade, sometimes drastically. For example, if the user enters an office building, the wireless signals can attenuate drastically, and further, due to reflection, refraction, multipath effects, etc., position accuracy using these wireless signals is reduced. When strength of wireless signals used for position estimation is weak or degraded, the user's position uncertainty tends to increase.
Kalman filters (KF) are known in the art for applications in positioning and navigation. Briefly, position estimation using a KF model involves a two-step process. In a first step, the KF produces an estimate of current state variables, along with their uncertainties. In a second step, an outcome is observed with the estimates, and based on the outcome, the estimates are updated, for example, using a weighted average, wherein higher weight is given to the estimates with lower uncertainties. Thus, the KF model involves a recursive algorithm, and can be applied in real time for position estimation using current state variables, for example, related to position (e.g., estimated geographical coordinates) and associated uncertainties. These estimates can be refined over time to obtain a user's position with higher accuracy. When strong wireless signals are available, positioning, for example, based on trilateration of satellite signals, will produce position estimates of high accuracy or low uncertainty. However, when the signals degrade, the uncertainties in position estimates increase.
In general, the uncertainty increases with time, and typical position estimation techniques tend to assume worst case conditions when dealing with uncertainties. For example, in a signal outage condition, traditional GNSS receivers assume that a user's dynamics pertain to the user travelling on a vehicle, instead of the user being a pedestrian, and this assumption would result in a significantly higher position uncertainty as a function of time while the outage persists. Existing techniques typically fail to correctly classify a user's motion as pedestrian motion where necessary, and thus the uncertainty growth is not properly checked.
Even if the user was correctly classified as a pedestrian in some cases, the conventional techniques continue to rely on worst case assumptions for estimating the user's position uncertainty. For example, worst case assumptions for a pedestrian user in this context would pertain to the assumption that the user would follow a straight line path throughout the duration of the signal outage. Following a straight line path from the last known accurate (or high precision) location of the user would get the user furthest away from the last known accurate location. In a more descriptive example, if the pedestrian user leaves an environment with strong wireless signals (e.g. outdoors with an open view to the sky), and enters an environment with weak or non-existent signal conditions (e.g. an office building with numerous walls and obstruction), the uncertainty in the user's position in a conventional KF model will be based on the user following a straight line path and the uncertainty would continue to monotonically increase until the user regains an accurate location fix. These assumptions lead to inaccuracies because the user's actual behavior is likely to differ from the worst case assumptions (e.g., if the worst case assumptions involved a straight line trajectory, but the user in fact remained stationary or moved around with many turns). The conventional KF models would have caused the uncertainty to increase needlessly to a large value.
Because of the high uncertainty accumulated, the time taken to overcome the high uncertainty and regain a more accurate position estimate when the user returns to strong signal conditions would be high. Moreover, a priori user position information, which may be stored by in some KF models with a view to improving subsequent user position estimates, cannot be leveraged effectively because of the high position uncertainty which can accumulate. Effectively, these shortcomings of conventional techniques lead to poor user experience.
Accordingly, there is a need for overcoming aforementioned problems associated with conventional handling of position uncertainty in the art of position estimation and navigation.